Biodiversity genomics research increasingly relies on multi-view visualization tools to support comparative analyses spanning species identification, evolutionary factors, hierarchical taxonomic relationships, wildlife forensics, population structure analysis, and conservation planning, which place distinct demands on visualization design. Despite the growing prevalence of multi-view layouts in the genomics domain, systematic understanding of how they support domain-specific biological goals through task abstractions remains limited. This systematic literature review addresses these gaps by establishing a domain-task mapping framework that explicitly connects biodiversity genomics biological goals to visualization task abstractions through an evidence-based software engineering approach. Applying a dual-task taxonomy that includes genomic-specific tasks and comparative actions, this review analyzed 34 papers identified through a structured search across five databases, using active-learning-assisted screening and an LLM-assisted data-extraction workflow. The results identify characteristic task-flow structures for four biological goals using Markov chain analysis and summarize their universal and unique structural patterns. Furthermore, five challenge-solution themes are documented across the included papers, encompassing visualization, tool features, tasks, data integration, and user interface and experience. These findings provide evidence-based guidance for visualization designers creating biodiversity genomics tools and bioinformaticians seeking to understand how multi-view designs support complex analytical workflows.
Many impactful personal decisions are made by individuals with little or no prior experience in decision making who must nevertheless navigate choices that are high stakes and complex. Unlike contexts where decision makers are experienced professionals, these individuals face unfamiliar situations in which they must weigh numerous trade-offs and preferences without clear guidance or prior knowledge. To investigate how multi-criteria decision making (MCDM) visualizations can support such individuals, we conducted two exploratory design studies: one with students making their first borrowing decisions and another with job seekers who had limited experience navigating the job market. Across both studies, participants initially focused narrowly on immediate, easily quantifiable aspects such as monthly repayment ability or yes/no filters on job attributes, while overlooking long-term consequences and more nuanced qualitative trade-offs. Our studies revealed several unstructured aspects of decision making in practice, including unclear preference hierarchies, difficulties in incorporating categorical and temporal criteria, and reliance on improvised datasets. Visualization probes encouraged participants to compare alternatives more systematically, surfacing neglected criteria and prompting more reflective reasoning. Together, these findings illustrate that personal, high-stakes decisions made in real-world contexts by individuals with limited prior experience challenge the fixed-data assumptions of MCDM and highlight new directions for visualization research. Supplementary materials are available at .
Financial literacy plays a vital role in everyday life, enabling individuals to manage spending, save for goals, and make sound financial decisions. However, for many people acquiring financial knowledge remains challenging due to the complexity of concepts and a lack of motivation. Data visualization offers a promising approach by helping learners better understand complex ideas and making learning more engaging. Despite its potential, few studies have thoroughly examined its application in financial education. This paper presents an exploratory analysis of the use of data visualization in this field. We analyze commonly used visualization tools and the dimensions of financial visualization education, develop taxonomies for key dimensions, identify their patterns and relationships. We also provide recommendations for future visualization tools in financial education. Furthermore, we propose a visualization-centric pedagogical framework for financial education. Unlike generic instructional design models, this framework is empirically grounded in our review and explicitly specifies where and how visualization techniques and tools should be embedded in the financial education process, thereby addressing the current lack of domain-specific guidance. Our findings aim to support financial educators and visualization professionals in applying effective visualization strategies, while also offering researchers a foundation for further investigation into the role of visualization in financial learning.
Missing data pose a widely known challenge for multivariate data analysis. Imputation is a mechanism for replacing missing values with plausible substitutes. Multiple imputation is often used to generate several plausible datasets for a wide range of statistical models to create robust standard errors and reduce potential bias from complete case analyses. Implementing an imputation algorithm requires several decisions about settings to generate meaningful imputed datasets. The analysis tasks can be summarized as analyzing attribute relations, analyzing the missingness mechanisms, supporting the selection of imputation parameters, and analyzing the imputation outcome. We propose a visual analytics pipeline that supports decision-making in each of the required steps. Each step is supported by novel visual encodings or existing ones that are adapted to the task. In particular, we adapt all methods to operate on heterogeneous data. Key visualizations are a 2D embedding of attribute similarities for heterogeneous data, a heatmap visualization providing a scalable overview of the effect of missingness on data distributions between all pairs of attributes (and between all original and all imputed attributes), and extended parallel coordinates that can handle both heterogeneous and missing data. Moreover, we propose a novel scheme for computing the imputation order based on an interactive predictor graph visualization. We illustrate the effectiveness of our visual encodings and the interactive visual analytics pipeline within workflows executed with domain experts from epidemiology to analyze cohort studies. The domain experts reported increased confidence in the imputation result due to improved transparency.
